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Building ETL Pipelines with Python

You're reading from   Building ETL Pipelines with Python Create and deploy enterprise-ready ETL pipelines by employing modern methods

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781804615256
Length 246 pages
Edition 1st Edition
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Authors (2):
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Brij Kishore Pandey Brij Kishore Pandey
Author Profile Icon Brij Kishore Pandey
Brij Kishore Pandey
Emily Ro Schoof Emily Ro Schoof
Author Profile Icon Emily Ro Schoof
Emily Ro Schoof
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1:Introduction to ETL, Data Pipelines, and Design Principles
2. Chapter 1: A Primer on Python and the Development Environment FREE CHAPTER 3. Chapter 2: Understanding the ETL Process and Data Pipelines 4. Chapter 3: Design Principles for Creating Scalable and Resilient Pipelines 5. Part 2:Designing ETL Pipelines with Python
6. Chapter 4: Sourcing Insightful Data and Data Extraction Strategies 7. Chapter 5: Data Cleansing and Transformation 8. Chapter 6: Loading Transformed Data 9. Chapter 7: Tutorial – Building an End-to-End ETL Pipeline in Python 10. Chapter 8: Powerful ETL Libraries and Tools in Python 11. Part 3:Creating ETL Pipelines in AWS
12. Chapter 9: A Primer on AWS Tools for ETL Processes 13. Chapter 10: Tutorial – Creating an ETL Pipeline in AWS 14. Chapter 11: Building Robust Deployment Pipelines in AWS 15. Part 4:Automating and Scaling ETL Pipelines
16. Chapter 12: Orchestration and Scaling in ETL Pipelines 17. Chapter 13: Testing Strategies for ETL Pipelines 18. Chapter 14: Best Practices for ETL Pipelines 19. Chapter 15: Use Cases and Further Reading 20. Index 21. Other Books You May Enjoy

New York Yellow Taxi data, ETL pipeline, and deployment

The previous exercise was a great example of refactoring legacy, less ideal implementations of ETL pipelines into clean ETL design pipelines. However, the datasets we used were quite simple and not entirely reflective of data you will come across in reality. It also lacked the pillars of unit testing and validation, which inevitably diminished the potential robustness of the pipeline.

In this scenario, we’ll take things a step further and build a pipeline that is more similar to what you might encounter in a professional setting. This pipeline will include professional coding practices, such as error handling, modularity for easy extension, and unit testing.

We will use New York 2021 Yellow Taxi Trip Data (https://data.cityofnewyork.us/Transportation/2021-Yellow-Taxi-Trip-Data/m6nq-qud6), an open source dataset that is significantly larger and more complex than the data in the previous example. It contains detailed...

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